#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMMessagesAppendFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams

# Load environment variables
load_dotenv(override=True)


# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        # set stop_secs to something roughly similar to the internal setting
        # of the Multimodal Live api, just to align events.
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        # set stop_secs to something roughly similar to the internal setting
        # of the Multimodal Live api, just to align events.
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        # set stop_secs to something roughly similar to the internal setting
        # of the Multimodal Live api, just to align events.
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot")

    # Create the Gemini Multimodal Live LLM service
    system_instruction = f"""
    You are a helpful AI assistant.
    Your goal is to demonstrate your capabilities in a helpful and engaging way.
    Your output will be converted to audio so don't include special characters in your answers.
    Respond to what the user said in a creative and helpful way.
    """

    llm = GeminiLiveLLMService(
        api_key=os.getenv("GOOGLE_API_KEY"),
        system_instruction=system_instruction,
        voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
    )

    # Build the pipeline
    pipeline = Pipeline(
        [
            transport.input(),
            llm,
            transport.output(),
        ]
    )

    # Configure the pipeline task
    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
    )

    # Handle client connection event
    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Kick off the conversation.
        await task.queue_frames(
            [
                LLMMessagesAppendFrame(
                    messages=[
                        {
                            "role": "user",
                            "content": f"Greet the user and introduce yourself.",
                        }
                    ]
                )
            ]
        )

    # Handle client disconnection events
    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")
        await task.cancel()

    # Run the pipeline
    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
    await runner.run(task)


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


if __name__ == "__main__":
    from pipecat.runner.run import main

    main()